“Wellbeing Through Reading”: The Impact of a Public Library and Healthcare Library Partnership Initiative in England
Bibliographic record
Abstract
Objective – This project sought to build upon a reader development tool, Many Roads to Wellbeing, developed by a health librarian in a mental health NHS Trust in Birmingham, England, by piloting reading group sessions in the main public library in the city using wellbeing-themed stories and poems. The aim was to establish whether a “wellbeing through reading” program can help reading group participants to experience key facets of wellbeing as defined by the Five Ways to Wellbeing. Methods – The program developers ran 15 monthly sessions at the Library of Birmingham. These were advertised using the Meetup social media tool to reach a wider client base than existing library users; members of the public who had self-prescribed to the group and were actively seeking wellbeing. A health librarian selected wellbeing-themed short stories and poems and facilitated read aloud sessions. The Library of Birmingham provided facilities and a member of staff to help support each session. Results – A total of 131 participants attended the 15 sessions that were hosted. There was a 95% response rate to the questionnaire survey. Of the respondents, 91% felt that sessions had helped them to engage with all of the Five Ways to Wellbeing. The three elements of Five Ways to Wellbeing that participants particularly engaged with were Connect (n=125), Take Notice (n=123), and Keep Learning (n=124). Conclusion – The reading program proved to be successful in helping participants to experience multiple dimensions of wellbeing. This project presents a new way of evaluating a bibliotherapy scheme for impact on wellbeing, as well as being an example of effective partnership working between the healthcare sector and a public library.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".